Explainable recommendation [1]}, [2]} has been studied from two major perspectives: human-computer interaction and machine learning.
The former [3]}, [4]}, [5]} investigates how people perceive different styles of explanations, while the latter provides explanations by designing new explainable recommendation algorithms, to which our work is more related.
There exist various types of explanation styles, such as pre-defined templates [1]}, [7]}, ranked sentences [8]}, [9]}, image visualizations [10]}, knowledge graph paths [11]}, [12]}, [13]}, [14]}, reasoning rules [15]}, [16]}, [17]}, etc., among which, recently, generated natural language explanations [18]}, [19]} have received much attention, mainly owing to the advancement of natural language generation technology and the availability of textual data on recommendation platforms such as e-commerce.
However, previous works mostly rely on recurrent neural networks (RNN), e.g., LSTM [20]} and GRU [21]}, leaving the potentially more effective Transformer under-explored, which motivates this work.
